What is meant by bias in data analytics?

Bias in data analytics refers to a systematic error that occurs due to flawed collection, processing, or analysis of data. This means that the results derived from the data can be skewed or misleading if the data is not representative of the true population or if certain aspects of the data collection method introduce some form of prejudice. For instance, if a survey only gathers responses from a specific demographic group, the findings might not be applicable to the entire population, leading to biased conclusions.

This systematic error can arise from various sources, such as sampling bias (where the sample does not represent the population), measurement bias (where the tools or methods used to gather data are flawed), or confirmation bias (where the analysis is influenced by pre-existing beliefs or expectations). Understanding and addressing bias is crucial in data analytics to ensure that insights drawn from the data are valid and reliable.

In contrast, the other choices describe concepts that do not align with the definition of bias. Enhancing algorithm performance, sorting data points, and visualizing data distributions pertain to methods and techniques within data analytics but do not capture the essence of bias, which is primarily concerned with errors in data representation and integrity.